SoroushVahidi/scidocs huggingface dataset:
Event Extraction with Complex Event Classification Using Rich Features
(2010)

Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge.
Message Understanding Conference- 6: A Brief History
(1996)

We have recently completed the sixth in a series of "Message Understanding Conferences" which are designed to promote and evaluate research in information extraction. MUC-6 introduced several innovations over prior MUCs, most notably in the range of different tasks for which evaluations were conducted. We describe some of the motivations for the new format and briefly discuss some of the results of the evaluations. 1 The M U C Evaluations We have just completed the sixth in a series of Message Understanding Conferences, which have been organized by NRAD, the RDT&E division of the Naval Command, Control and Ocean Surveillance Center (formerly NOSC, the Naval Ocean Systems Center) with the support of DARPA, the Defense Advanced Research Projects Agency. This paper looks briefly at the history of these Conferences and then examines the considerations which led to the structure of MUC-6} The Message Understanding Conferences were initiated by NOSC to assess and to foster research on the automated analysis of mili tary messages containing textual information. Although called "conferences", the distinguishing characteristic of the MUCs are not the conferences themselves, but the evaluations to which participants must submit in order to be permit ted to at tend the conference. For each MUC, participating groups have been given sample messages and instructions on the type of information to be extracted, and have developed a system to process such messages. Then, shortly before the conference, participants are given a set of test messages to be run through their system (without making any changes to the system); the output of each part icipant 's system 1The full proceedings of the conference are to be distributed by Morgan Kaufmann Publishers, San Mateo, California; earlier MUC proeeedings~ for MUC-3, 4, and 5, are also available from Morgan Kaufmann. Beth Sundhe im Naval Command, Control and Ocean Surveillance Center Research, Development, Test and Evaluation Division (NRaD) Code 44208 53140 Gatchell Road San Diego, CMifornia 92152-7420 s u n d h e i m @ p o j k e . n o s c . m i l is then evaluated against a manual ly-prepared answer key. The MUCs are remarkable in part because of the degree to which these evaluations have defined a prograin of research and development. DARPA has a number of information science and technology programs which are driven in large part, by regular evaluations. The MUCs are notable, however, in that they in large par t have shaped the research program in information extraction and brought it to its current s ta te}
Inducing Features of Random Fields
(1995)

We present a technique for constructing random elds from a set of training samples. The learning paradigm builds increasingly complex elds by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the eld and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random eld models and techniques introduced in this paper di er from those common to much of the computer vision literature in that the underlying random elds are nonMarkovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classi cation in natural language processing.
Memory consolidation and the hippocampus: further evidence from studies of autobiographical memory in semantic dementia and frontal variant frontotemporal dementia
(2002)

Studies of autobiographical memory in semantic dementia have found relative preservation of memories for recent rather than remote events. As semantic dementia is associated with progressive atrophy to temporal neocortex, with early asymmetric sparing of the hippocampus, this neuropsychological pattern suggests that the hippocampal complex plays a role in the acquisition and retrieval of recent memories, but is not necessary for the recall of older episodic events. In an alternative view of memory consolidation, however, the hippocampus plays a role in the retrieval of all autobiographical memories, regardless of the age of the memory [Curr. Opin. Neurobiol. 7(1997)217]. This 'multiple trace theory' predicts that patients with semantic dementia should show no effects of time in their autobiographical recall. In this article, we ask whether it is possible to reconcile the data from semantic dementia with the multiple trace theory by investigating whether the time-dependent pattern of autobiographical retrieval seen in the disease is due to (i) patients showing this effect being exceptional in their presentation; and/or (ii) patients with semantic dementia exhibiting impaired strategic retrieval from concomitant frontal damage. A series of experiments in patients with semantic dementia, the frontal variant of frontotemporal dementia and Alzheimer's disease clearly demonstrates that neither of these two factors can explain the documented effect of time seen in semantic dementia. Nonetheless, we discuss how damage to semantic knowledge could result in an autobiographical memory deficit and suggest that data from semantic dementia may be consistent with both views of hippocampal involvement in long-term memory.
An integrated GPU power and performance model
(2010)

GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Performance optimization for multi-core processors has been a challenge for programmers. Furthermore, optimizing for power consumption is even more difficult. Unfortunately, as a result of the high number of processors, the power consumption of many-core processors such as GPUs has increased significantly.
 Hence, in this paper, we propose an integrated power and performance (IPP) prediction model for a GPU architecture to predict the optimal number of active processors for a given application. The basic intuition is that when an application reaches the peak memory bandwidth, using more cores does not result in performance improvement.
 We develop an empirical power model for the GPU. Unlike most previous models, which require measured execution times, hardware performance counters, or architectural simulations, IPP predicts execution times to calculate dynamic power events. We then use the outcome of IPP to control the number of running cores. We also model the increases in power consumption that resulted from the increases in temperature.
 With the predicted optimal number of active cores, we show that we can save up to 22.09%of runtime GPU energy consumption and on average 10.99% of that for the five memory bandwidth-limited benchmarks.
Growth of the flickr social network
(2008)

Online social networking sites like MySpace, Orkut, and Flickr are among the most popular sites on the Web and continue to experience dramatic growth in their user population. The popularity of these sites offers a unique opportunity to study the dynamics of social networks at scale. Having a proper understanding of how online social networks grow can provide insights into the network structure, allow predictions of future growth, and enable simulation of systems on networks of arbitrary size. However, to date, most empirical studies have focused on static network snapshots rather than growth dynamics.
 In this paper, we collect and examine detailed growth data from the Flickr online social network, focusing on the ways in which new links are formed. Our study makes two contributions. First, we collect detailed data covering three months of growth, encompassing 950,143 new users and over 9.7 million new links, and we make this data available to the research community. Second, we use a first-principles approach to investigate the link formation process. In short, we find that links tend to be created by users who already have many links, that users tend to respond to incoming links by creating links back to the source, and that users link to other users who are already close in the network.
Characterizing Data Structures for Volatile Forensics
(2011)

Volatile memory forensic tools can extract valuable evidence from latent data structures present in memory dumps. However, current techniques are generally limited by a lack of understanding of the underlying data without the use of expert knowledge. In this paper, we characterize the nature of such evidence by using deep analysis techniques to better understand the life-cycle and recoverability of latent program data in memory. We have developed Cafegrind, a tool that can systematically build an object map and track the use of data structures as a program is running. Statistics collected by our tool can show which data structures are the most numerous, which structures are the most frequently accessed and provide summary statistics to guide forensic analysts in the evidence gathering process. As programs grow increasingly complex and numerous, the ability to pinpoint specific evidence in memory dumps will be increasingly helpful. Cafegrind has been tested on a number of real-world applications and we have shown that it can successfully map up to 96% of heap accesses.
Sparse Model-based Image Alignment Feature Alignment Pose & Structure Refinement Motion Estimation Thread New Image Last Frame Map Frame Queue Feature Extraction Initialize Depth-Filters
(2015)

Direct methods for Visual Odometry (VO) have gained popularity due to their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, where established feature-based methods instead succeed at. Based on these considerations, we propose a Semi-direct VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy. SUPPLEMENTARY MATERIAL Video of the experiments: https://youtu.be/hR8uq1RTUfA
Autonomic nervous system activity in emotion: A review
(2010)

Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed.
Dude, where's my card?: RFID positioning that works with multipath and non-line of sight
(2013)

RFIDs are emerging as a vital component of the Internet of Things. In 2012, billions of RFIDs have been deployed to locate equipment, track drugs, tag retail goods, etc. Current RFID systems, however, can only identify whether a tagged object is within radio range (which could be up to tens of meters), but cannot pinpoint its exact location. Past proposals for addressing this limitation rely on a line-of-sight model and hence perform poorly when faced with multipath effects or non-line-of-sight, which are typical in real-world deployments. This paper introduces the first fine-grained RFID positioning system that is robust to multipath and non-line-of-sight scenarios. Unlike past work, which considers multipath as detrimental, our design exploits multipath to accurately locate RFIDs. The intuition underlying our design is that nearby RFIDs experience a similar multipath environment (e.g., reflectors in the environment) and thus exhibit similar multipath profiles. We capture and extract these multipath profiles by using a synthetic aperture radar (SAR) created via antenna motion. We then adapt dynamic time warping (DTW) techniques to pinpoint a tag's location. We built a prototype of our design using USRP software radios. Results from a deployment of 200 commercial RFIDs in our university library demonstrate that the new design can locate misplaced books with a median accuracy of 11~cm.
Activity Recognition with Smartphone Sensors Second Exam Literature Review
(2014)

The ubiquity of smartphones together with their ever-growing computing, networking and sensing powers have been changing the landscape of people’s daily life. Among others, activity recoginition, which takes the raw sensor reading as inputs and predicts a user’s motion activity, has become an active research area in recent years. It is the core building block in many high-impact applications, ranging from health and fitness monitoring, personal biometric signature, urban computing, assistive technology and elder-care, to indoor localization and navigation, etc. This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones’ sensors. We start with the basic concepts such as sensors, activity types, etc. We review the core data mining techniques behind the main stream activity recognition algorithms, analyze their major challenges and introduce a variety of real applications enabled by activity recognition. Professor: Dr. Hanghang Tong Signature: Date: 06-12-2014 Professor: Dr. Ping Ji Signature: Date: 06-12-2014 Professor: Dr. Ted Brown Signature: Date: 06-12-2014 Advisor: Dr. Hanghang Tong
Chinese character CAPTCHA recognition based on convolution neural network
(2016)

CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are increasingly used in many applications for machine and human identification. Compared with traditional English and digital characters based CAPTCHAs, Chinese characters contain more complicated characters which greatly enhance difficulty of automatic recognition. To solve that problem, we proposed a Convolution Neural Network (CNN) based approach. This approach greatly improves the recognition accuracy of Chinese Character CAPTCHAs with distortion, rotation and background noise. Our experiment results show that this approach achieves more than 95% accuracy for single character and 84% accuracy for three types of Chinese Character CAPTCHAs with four characters. This encouraging result indicates that deep neural network is useful in complicated structure perception of Chinese Character CAPTCHAs.
Deep learning with support vector data description
(2015)

One of the most critical problems for machine learning methods is overfitting. The overfitting problem is a phenomenon in which the accuracy of the model on unseen data is poor whereas the training accuracy is nearly perfect. This problem is particularly severe in complex models that have a large set of parameters. In this paper, we propose a deep learning neural network model that adopts the support vector data description (SVDD). The SVDD is a variant of the support vector machine, which has high generalization performance by acquiring a maximal margin in one-class classification problems. The proposed model strives to obtain the representational power of deep learning. Generalization performance is maintained using the SVDD. The experimental results showed that the proposed model can learn multiclass data without severe overfitting problems.
Building Footprints Extraction from Oblique Imagery
(2013)

Nowadays, multi-camera aerial platforms combining nadir and oblique cameras are experiencing a revival and several companies have proposed new image acquisition systems. Due to their various advantages, oblique imagery have found their place in numerous companies and civil applications. However, the automatic processing of such image blocks still remains a topic of research. Camera configuration indeed poses a challenge on the traditional photogrammetric pipeline used in commercial software but, on the other hand, gives the opportunity to exploit the additional information provided by the oblique views and allows a more reliable feature extraction. In particular, the information that can be provided in correspondence of building façades can open new possibilities for the building detection and footprint extraction. In this paper, a methodology for the automated extraction of building footprints from oblique imagery is presented. The extraction is performed using dense point clouds generated using an image matching algorithm. The developed methodology and the achieved results are described in detail showing the advantages and opportunities offered by oblique aerial systems for cartographic and mapping purposes.
Multi-View Dimensionality Reduction via Canonical Correlation Analysis
(2008)

We analyze the multi-view regression problem where we have two views X = (X, X) of the input data and a target variableY of interest. We provide sufficient conditions under which we can reduce the dimensionality of X (via a projection) without loosing predictive power of Y . Crucially, this projection can be computed via a Canonical Correlation Analysis only on the unlabeled data. The algorithmic template is as f ollows: with unlabeled data, perform CCA and construct a certain projection; with the labeled data, do least squa res regression in this lower dimensional space. We show how, under certain natural assumptions, the number o f labeled samples could be significantly reduced (in comparison to the single view setting) — in particular, we show how this dimen sionality reduction does not loose predictive power ofY (thus it only introduces little bias but could drastically reduce the variance). We explore two separate assumptions under which this is possible and show how, under either assumption alone, dimensionality reduction could reduce the labeled sample complexity. The two assumptions we consider are a conditional independenceassumption and a redundancyassumption. The typical conditional independence assumption is that conditioned onY the viewsX andX are independent — we relax this assumption to: conditioned on some hidden state H the viewsX andX are independent. Under the redundancy assumption, we show that the best predictor from each view is roughly as good as the best predictor u sing both views.
High Performance Ternary Multiplier Using CNTFET
(2018)

Ternary logic is a promising alternative to the conventional binary logic in VLSI design as it provides the advantages of reduced interconnects, higher operating speeds and smaller chip area. This work presents a ternary multiplier using carbon nanotube field effect transistors (CNTFETs). The proposed designs use in-depth analysis of addition required for designing a two trit multiplier. Based on this analysis two efficient adders are proposed. These adders are used to optimize the multiplier design. The proposed circuits are extensively simulated using HSPICE to obtain power, delay and power delay product. The circuit performances are compared with designs reported in recent literature. This circuit demonstrates a power delay product improvement up to 16.8%, with lesser transistor count of 16%. So, the use of these circuits in complex arithmetic circuits will be advantageous.
Informative Path Planning and Mapping with Multiple UAVs in Wind Fields
(2016)

Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating under a set of constraints, such as dynamic feasibility of vehicles. The key challenges of IPP are the strong coupling in multiple layers of decisions: the selection of locations to visit, the allocation of sensor platforms to those locations; and the processing of the gathered information along the paths. This paper presents an systematic procedure for IPP and environmental mapping using multiple UAV sensor platforms. It (a) selects the best locations to observe, (b) calculates the cost and finds the best paths for each UAV, and (c) estimates the measurement value within a given region using the Gaussian process (GP) regression framework. An illustrative example of RF intensity field mapping is presented to demonstrate the validity and applicability of the proposed approach.
A new pedestrian detection method based on combined HOG and LSS features
(2015)

Pedestrian detection is a critical issue in computer vision, with several feature descriptors can be adopted. Since the ability of various kinds of feature descriptor is different in pedestrian detection and there is no basis in feature selection, we analyze the commonly used features in theory and compare them in experiments. It is desired to find a new feature with the strongest description ability from their pair-wise combinations. In experiments, INRIA database and Daimler database are adopted as the training and testing set. By theoretic analysis, we find the HOG–LSS combined feature have more comprehensive description ability. At first, Adaboost is regarded as classifier and the experimental results show that the description ability of the new combination features is improved on the basis of the single feature and HOG–LSS combined feature has the strongest description ability. For further verifying this conclusion, SVM classifier is used in the experiment. The detection performance is evaluated by miss rate, the false positives per window, and the false positives per image. The results of these indicators further prove that description ability of HOG–LSS feature is better than other combination of these